Alice Ching

On April 18th, just last week, Meta unveiled Llama 3, lauded as "the most capable openly available LLM to date". This remarkable achievement by the Meta team comes hot on the heels of Llama 2's release last July. Notably, the new models surpass their predecessors and competing offerings from other providers in terms of performance. Meta's commitment to open sourcing these powerful models while prioritizing model safety and responsible usage is commendable. (https://lnkd.in/ghv3RJRz) Yesterday, Microsoft introduced Phi-3 Mini, a member of the Phi-3 family of models, promoted as "the most capable and cost-effective small language models available". According to Microsoft, the key to achieve high performance in such a small package lies in the quality of the training data. The model has been open sourced and is now available on Ollama and Hugging Face. (https://lnkd.in/gKgSmrBn)   These compact yet powerful models serve as invaluable tools for creators eager to bring their visions to life or simply experiment with cutting-edge technology, particularly for those with limited access to resources.   Shortly after the release of Llama 3, reports began surfacing of enthusiasts running the 8B model on a Raspberry Pi 5 equipped with just 8GB of RAM—a modest $80 single-board computer. (https://lnkd.in/gbeQPhQ2) While the performance understandably reflects the limitations of such a platform, the fact that the model runs and generates an output is nothing short of magical. Similarly, the Phi-3 Mini model can run on ubiquitous devices such as smartphones.   Reflecting on my own journey, I recall the pivotal role that early access to a low-cost computer played in shaping my passion for coding. The gift of an 8-bit Commodore 64 home computer ignited my curiosity at a young age. Later, with the guidance of an outstanding Computer Science teaching staff at my high school IISS Marconi-Hack Bari, I embarked on ambitious projects like a networked multiplayer version of Battleship written in Turbo Pascal and 8086 Assembly, complete with custom sprites and lo-res graphics. These formative experiences paved the way for my career, eventually leading me to various engineering and leadership roles.   It all began with a simple, low-cost, modestly capable computer used for experimentation—a gateway to discovering my life's passion.   I encourage educators and parents alike to consider setting up similar low-cost experimentation environments. By introducing younger generations to open-source AI technologies, we can nurture their creativity and help them uncover their true passions. It's not just a weekend project—it's an investment in their future and the future of innovation and discovery. #education #youth #AI #LLM #future #innovation

24

Also, we released a new version of the public beta, which is available in Creative Cloud in the "Beta Apps" section under Applications. No new features this time--we are still iterating on the Assets Library Panel and "Search by Shape" feature, as well as the "Smart Upres" feature. If you haven't tried either of these, please do and let us know what you think! Here is how you can access the beta: https://lnkd.in/gq3uqEdm

3

Just like the big boys, bookend.ai now supports Llama3, alongside our menagerie of open source LLMs. While Llama3 is great, it's also just the tip of the iceberg. What will truly separate the herd from the herd-wranglers is going to take a lot more work than house training a llama. Real value lies in automating the real-life, workhorse apps that power the enterprise and that requires maximizing both safety and simplicity without compromising accuracy and flexibility.

16

**Day 2 as a Headstarter AI fellow with Jack O'Brien** Today, I had the pleasure of being introduced to Jack O'Brien (former Google software Engineer and tech lead, & former CTO of A16z-backed company “stonks”). In todays meeting Jack O'Brien shared valuable insights on how to acquire your first users, build a waitlist, and generate your first $1,000 in revenue. Here are some key takeaways. 1. **Create something cool enough**: Develope something as little as a landing page to get people to sing up and test if people would actually use your product. 2. **Leverage your network**: Get friends, family, and acquaintances to use your product and provide feedback. 3. **Promote Widely**: Share your product all over social media to gain visibility and attract users. A huge thank you to Jack O'Brien for the insightful session and to the Headstarter AI team Faizan Ahmed Yasin Ehsan 🚀 for organizing another great day of learning!

50

1 Comment

Meta Introduces Chameleon: A State-of-the-Art Multimodal Model 🦎 Meta has unveiled Chameleon, a groundbreaking multimodal model that pushes the boundaries of AI capabilities. Chameleon seamlessly integrates vision and language, enabling it to understand and generate images and text with remarkable accuracy. Key Takeaways: 🎨 Chameleon excels in both image captioning and text-to-image generation, showcasing its versatility across multiple tasks. 🧠 The model leverages a novel architecture called Mixture-of-Attention (MoA), which efficiently processes and combines visual and textual information. 🌐 Chameleon outperforms existing state-of-the-art models in zero-shot image captioning and text-to-image generation on various benchmarks. 🔍 The model's ability to handle complex, compositional prompts sets it apart from other multimodal models. 🔒 Meta has open-sourced Chameleon's code and trained weights, fostering transparency and enabling further research in the field. Chameleon represents a significant milestone in the development of multimodal AI systems. Its ability to understand and generate both images and text opens up exciting possibilities for applications in fields such as creative content generation, assistive technologies, and more. Read the full article to learn more about Chameleon and its implications for the future of AI: https://lnkd.in/gk_ZTZdR #chameleon #multimodalai #artificialintelligence #ai #machinelearning #ml

5

Having lived in SF bay area for so long, I came to know the big coffee culture scene here. I was totally consumed by it a while ago and spent years trying to attain the “perfect" latte. I published a fun coffee story and my entire home setup, which took weeks of research and multiple iterations, in my blog link in bio. Meanwhile, to all fellow coffee loving tech peeps, here are some top tips that no one told me. Put it in practice at home or in microkitchens. - A big factor in preparing milk suitable for latte art is pressure of the wand. If this is important to you, you need to get a machine that has a strong enough pressure. - When pulling shots, put in 18g of beans and extract 30g of espresso. - The darker the roast, the coarser the setting should be on the grinder, the lighter the roast, the finer the setting should be. - When adjusting the grind setting, do so while the grinder is working if there are beans in the hopper. Otherwise, a bean can get stuck between the burrrs. - Pulling a shot should take 20-30 sec in time, or based on observation. It should look like caramel going out, not a waterfall. - Eventually work your way to using a bottomless portafilter. It punishes you for all the small imperfections when preparing your coffee puck, like any tilt, and uneven distribution. It does so by spraying the espresso all over the place. The more you can get it to flow uniformly, the better your preparation of espresso, objectively. - Seal your beans in a vacuum, so they don’t go stale. The more stale the beans, the more minor adjustment you need to make to grind finer. - When steaming milk in your pitcher, you can tell if it’s done by holding the pitcher and as soon as the heat becomes unbearable i.e. stings, it’s done. - Taking a latte art course at a local coffee shop was the best boost for me to start nailing latte art.

9

This dialogue I had with Claude.ai is a very simple illustration of the importance of discourse with our AI colleagues. I knew the headline was grammatically incorrect, but wanted to see what Claude thought. And I challenged Claude when I--as a human--knew better These are important interactions to be had with robust AIs like Claude. Here's the discourse: Jen: This is a news article headline. Is it grammatically correct? "Record amount of preventative fires set in California this year — but is it enough?" Claude: The headline is mostly grammatically correct, but there's a minor error. The correct word should be "preventive" rather than "preventative." While both words are sometimes used interchangeably in casual speech, "preventive" is generally considered the standard form in formal writing and journalism. A corrected version would be: Record amount of preventive fires set in California this year — but is it enough? The rest of the headline structure is correct. It uses an em dash (—) to separate the two clauses, which is a common stylistic choice in headlines. The capitalization follows standard headline capitalization rules, and the question at the end is properly punctuated. Would you like me to explain any other aspects of the headline's structure or content? Jen: I actually wonder if it should read "record NUMBER of preventive fires..." Focus on difference between "amount" and "number" Claude: You've made an excellent observation. You're absolutely correct to focus on the distinction between "amount" and "number." Let's examine this aspect: "Amount" is typically used for uncountable nouns or mass quantities, while "number" is used for countable items. In this case, fires are discrete, countable entities. Therefore, "number" is indeed the more appropriate choice. The headline should read: Record number of preventive fires set in California this year — but is it enough? This version is more precise and adheres better to standard English usage. Fires can be counted individually, so we refer to them in terms of "number" rather than "amount." Thank you for bringing attention to this important grammatical distinction. It's a common error that many people overlook, but using the correct term enhances clarity and accuracy in writing.

4

1 Comment

I ran across this post in the Changelog newsletter: https://lnkd.in/eCdjVekq - I like the name slop for unwanted AI-generated content. I love LLMs. They are part of my daily workflow, and I've been exploring them more seriously while on my sabbatical. I wouldn't misrepresent generated content without critical review as my own, but the web seems to be filled with more generated garbage (or 'slop')—content that looks fine at first but isn't accurate or helpful. LLMs (and other generative AI) should help us to make things of higher quality, not just churn out slop more quickly.

11

1 Comment

Llama3 is out. It's the new open weights model from Meta, and you can try it out for free at https://www.meta.ai/. Ollama had it ready to go within hours (https://lnkd.in/eJks-Vg2). If you grabbed a copy from Ollama within the first ~5 hours, pull again; they fixed an incorrect end token issue. In my last post, I suggested that stiff competition between models would lead to competing on price. Meta.ai is now free, even without a Facebook login. Initial claims and benchmarks put Llama3 on par with private models in many areas – a significant development. The proprietary vs. open model gap is closing, thanks to Meta's commitment to top-tier open weight models. I can't imagine where the open LLM community would be without Meta's financial commitment. I tried Llama-3-8B and a low-quant Llama-3-40B briefly last night. While I haven't formed a solid opinion yet, the benchmarks claim Llama-3-8B outperforms Llama-2-40B. If that holds true, it's a significant win, as it's not just about throwing more compute at the problem – making models that can run on consumer hardware better is a big deal. While a 40B model is currently out of reach for most consumers, it's easy to imagine that it will become increasingly accessible as new video cards with higher memory become the norm. I managed to run Llama-3-40B on a 2quant on a 3090 without running out of RAM. I don't have high hopes for great results, but after some tuning, models based on Llama-3-40B could make for a very reasonable private assistant on modest hardware. If you've tried Llama-3 let me know your experience so far. Also if you have any tips for the best options for a 3090 let me know.

2

Maybe it's a hot take, but I've always found production support rotation to be an engaging and low-stress part of building software. Production support is a critical part of any startup's process (at least if you have users). But beyond the process, production support *culture* has a huge impact on the happiness of both your customers and your team. Here's how I frame it to keep things engaging, rewarding and low-stress. https://lnkd.in/eFd9MCcu

12

🍜Anybody up for some ramen? I am excited to share that I am back in the garage, building again (it’s actually a house in the Berkeley Hills). We are taking the wraps off of Supercog AI, a new startup focused on solving Application Integration using LLM powered agents. My co-founder in this new venture is a great friend of mine (and a former co-founder partner), Alex Osborne. Alex and I have worked around the application integration space for a long time, and we are unrealistically excited about applying the GenAI stack to this problem. Not many people relish the task to get some bits to move from system A to system B. But it’s a critical job to be done, and that’s led to a lot of brittle scripts, a myriad of inflexible “no code” tools, and a lot of hand crafted SQL. But the power of the Large Language Model, trained on a huge corpus of information, offers a remarkable tool to solve this problem. The LLM you use today already knows the APIs of hundreds of popular systems. It knows the SQL dialects of every major database. It understands the semantics and data schemas of many popular SaaS systems. It understands much of the specific domain in which your business operates. I like to call the AI revolution “the last platform shift”. That may turn out to be hyperbole, but I definitely believe we are in the very early stages of seeing what this new stack can enable. I know we are hardly the first folks to claim that “amazing stuff is coming!”. But this is why Alex and I have decided to focus on a real and hard problem. If we can prove that LLMs can power a new way to solve this problem, it will be the first successful *new* approach in 15 years.. We will have much more to share about this new platform soon. In the meantime, if you’re interested in getting a peek - or even better if you have application or data integration tasks that you would like some help with - please reach out because we want to help. #genai #startups #backinthegarage

163

37 Comments

Google Wants to Make AI Your Literal Co-Worker At Google's I/O developer conference, company leaders unveiled their all-in plans for AI. It's a lot. BY BEN SHERRY, STAFF REPORTER @BENLUCASSHERRY MAY 14, 2024 Illustration: Inc., Google is bringing AI to your Gmail. And to your Sheets. And to your favorite rapper. At Google's annual I/O developer conference, CEO Sundar Pichai said the company is in its "Gemini Era," referring to the search giant's flagship, multimodal AI model (and to Taylor Swift's Eras Tour, of course). Indeed, the company is going all-in on generative AI, and multiple Google leaders explained how the tech will help people work faster and smarter. 

1

More features in Shorts Creation tool!!

12

Backstage from Spotify hosted a "Roadmap webinar" today. Spotify Portal for Backstage was the clear 💫 superstar 💫 of the presentation. I talked to several teams using Backstage recently, to varying degrees of success. All have struggled to get over the initial hurdle of adding catalog-info.yaml files to each of their repos. It's clear that Backstage's evolution is hitting the right notes here. That said, I'm still skeptical. Will opening PRs and providing simple context be enough to help these teams actually get the benefits Backstage? Backstage comes with massive amounts of ongoing maintenance that Portal doesn't tackle, and legacy setups and monorepos that probably every team has somewhere in their infrastructure don't quite fit into Backstage's perfect world one-click setup model. Spotify is walking the line between: - Full extensibility with massive effort to setup and maintain - Highly opinionated, easier to setup, but fewer capabilities Clearly I think Backstage is on the right track, but I'm not sure how they're going to pull it all together. How will they make the ingested data more meaningful? How will they make their product more accessible, especially those who don't have large DevEx or Platform teams? Do you have your own thoughts about Platforms, Portals, or developer enablement? The last few weeks talking with engineering, platform, and DevEx leaders have been some of the most fun of my career. I'd love to hear from you.

29

1 Comment

Hot off the press, this latest Silicon Valley Product Group article is a good reminder that there are multiple paths to success, and we have to tailor our strategies to fit our unique circumstances. There’s rarely a single answer or approach when it comes to topics like organizational design— after all, people and companies are complicated and are never the same! To quote Marty: “I believe strongly that with the right people with the right skills, a strong product team can succeed no matter the org design. […] I will never again doubt what a motivated group of empowered and skilled cross-functional professionals can accomplish.” Definitely worth a read if you have ~4 minutes and lead or are part of a product team.

4

It’s been fascinating learning how to build products with LLMs. This is a really good collection of lessons that resonated. https://lnkd.in/eivFPPAF

11

2 Comments

Runway Gen 3 #AI #video generation is out, if you want to stay in the loop on what is possible then check out this prompt exploration with Purz who spends his time showing the world what is possible https://lnkd.in/gZD-bX7p

5

Performance is key! As we cannot share the detailed work with (former) customers of Rockset (acquired by OpenAI), we decided to implement the Rockbench benchmark to check how streaming ingest between CrateDB and Rockset (acquired by OpenAI) compares. The results are very insightful. We see: ✅ CrateDB achieves 6-9x lower latencies than Rockset for streaming ingest. ✅ CrateDB outperforms Rockset on the same hardware while saving about 20% costs. ✅ When volumes increase, the latency increased linearly in Rockset, while remaining mostly flat in CrateDB. As a plus: 1️⃣ Achieve full high availability due to the distributed nature of CrateDB (benchmark results are achieved including replication in CrateDB) 2️⃣ Achieve higher throughput on the same amount of CPUs as Rockset (at 50% of memory needs) 3️⃣ Full open-source solution that prevents you from market shifts On side note: If you are thinking to switch to Elastic/OpenSearch, this benchmark has been created to compare Rockset vs. Elastic, so CrateDB is also faster than Elastic for streaming ingest cases! Read the full comparison here: https://lnkd.in/d_nEMDkx

29

1 Comment